4D interactive mission analytics for visualization of unmanned vehicle performance indicators

ABSTRACT

A system includes an analytics collector that receives world state data to provide status relating to a plurality of mission analytics of an unmanned vehicle or an unmanned vehicle mission planner. An asset filter filters the status from the analytics collector with respect to mission analytics of a subset of selected assets. An analytic aggregator collects the filtered status from the asset filter and generates a visual analytics file based on one or more selected analytics for the subset of selected assets. A rendering pipeline processes the visual analytics file from the analytic aggregator and generates a formatted output file describing a visualization of the plurality of mission analytics from the visual analytics file.

RELATED APPLICATIONS

This application is a continuation application claiming priority fromU.S. patent application Ser. No. 15/204,690, filed 7 Jul. 2016, which isincorporated herein in its entirety.

TECHNICAL FIELD

This disclosure relates to unmanned vehicle interfaces, and moreparticularly to dynamic visualization of performance indicators formanned and unmanned vehicles or vehicle swarms that include manned andunmanned teams over selected time periods.

BACKGROUND

Typical mission plan interface software for a general manned or unmannedvehicle allows operators to plan the tasks to be executed by eachvehicle via a graphical user interface that includes various input andoutput options for feedback and control of the planning process. Thegraphical user interface typically provides a three-dimensionalpresentation that includes latitude, longitude, and altitude informationin the display output relating to a proposed mission plan duringpre-mission planning and also allows monitoring and control of real-timemission progress during mission execution. This includes updating waypoints, aborting plans, allowing manual override, and so forth. Typicalgraphical user interfaces to existing mission planning and controlsystems have limited abilities to analyze how changes to the currentenvironment will impact tasks planned for execution in the future. Thisusually consists of limited abilities to visually compare the currentiteration of a single plan for a single vehicle with the previousiteration of the same plan. Comparison is typically performed visuallyby a human operator comparing the route and each individual task withinthe mission plan against defined mission success criteria. Some methodsattempt to provide computational metrics that quantify the performanceof one or more aspects of the mission plan. These current methods areslow and manually cumbersome at evaluating the mission plan'sperformance for a single vehicle. Current methods are also typicallytied to a single mission planner designed for a single specific vehiclefurther restricting their ability to evaluate alternatives for othervehicles of the same domain type or other vehicles of different domaintypes.

The current state of vehicle interface software creates a bottleneckwhen planning and managing the execution of a plurality of vehicles aseach plan for each vehicle must be manually coordinated with the plansof all other vehicles involved in satisfying the same set of tasks. Thisbottleneck becomes a serious constraint when the number of vehiclesexceeds two or three and is further exacerbated when the plurality ofvehicles is heterogeneous (of different types) and a mixture of mannedand unmanned teams.

SUMMARY

This disclosure relates to dynamic visualization of performanceindicators for manned and unmanned vehicles or vehicle swarms of mannedand unmanned teams over selected time periods. In one aspect, a systemincludes a memory to store computer-executable components and aprocessor to execute the computer-executable components from the memory.The computer-executable components include an analytics collector thatreceives world state data to provide status relating to a plurality ofmission analytics of an unmanned vehicle or an unmanned vehicle missionplanner. The mission analytics relate to the ability of the unmannedvehicle and its associated payload resources to execute a given missionplan. An asset filter filters the status from the analytics collectorwith respect to mission analytics of a subset of selected assets. Anasset selector specifies the subset of selected assets to the assetfilter. An analytic aggregator collects the filtered status from theasset filter and generates a visual analytics file based on one or moreselected analytics for the subset of selected assets.

An analytic selector specifies the one or more selected analytics to theanalytic aggregator. A rendering pipeline processes the visual analyticsfile from the analytic aggregator and generates a formatted output filedescribing a visualization of the plurality of mission analytics fromthe visual analytics file. The formatted output file is generated as acollection of display objects. Each of the display objects represent atleast one mission analytic from the one or more selected analytics ofthe subset of selected assets. Each of the specified display objectsrepresent a current or future status of the mission analytics over aspecified time window.

In another aspect, a non-transitory computer readable medium havingcomputer executable instructions stored thereon. The computer executableinstructions to provide status relating to a plurality of missionanalytics of a plurality of unmanned vehicles and a plurality ofunmanned vehicle mission planners. The mission analytics relate to theability of the plurality of unmanned vehicles and their respectivepayload resources to execute a respective mission plan or respectivealternative mission plan generated by the plurality of unmanned vehiclemission planners. The instructions filter the status with respect tomission analytics of a subset of selected assets and specify the subsetof selected assets. The instructions collect the filtered status fromthe asset filter and generate a visual analytics file based on one ormore selected analytics for the subset of selected assets. Theinstructions specify the one or more selected analytics. Theinstructions process the visual analytics file and generate a formattedoutput file describing a visualization of the plurality of missionanalytics from the visual analytics file. The formatted output file isgenerated as a collection of display objects. Each of the displayobjects represent at least one mission analytic from the one or moreselected analytics of the subset of selected assets. Each of thespecified display objects represent a current or future status of themission analytics over a specified time window.

In yet another aspect, a method includes providing status relating to aplurality of mission analytics of an unmanned vehicle or an unmannedvehicle mission planner. The mission analytics relate to the ability ofthe unmanned vehicle and its associated payload resources to execute agiven mission plan. The method includes generating an analytics filefrom the status describing the plurality of mission analytics. Themethod includes generating a formatted output file from the analyticsfile describing a visualization of the plurality of mission analyticsfrom the analytics file. The formatted output file is generated as acollection of display objects. Each of the display objects represent atleast one mission analytic from the plurality of mission analytics. Themethod includes specifying a subset of the display objects to bevisualized in the formatted output file. The method includes generatinga mission analytics visualization output from the subset of displayobjects specified via the formatted output file. Each of the specifieddisplay objects represent a current or future status of the missionanalytics over a specified time window.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example system for dynamicvisualization of performance indicators for manned and unmanned vehiclesor manned and unmanned vehicle swarms over selected time periods.

FIG. 2 illustrates an example mission analytics interface for dynamicvisualization of performance indicators and switching controls forchanging views between unmanned vehicles or vehicle swarms over selectedtime periods.

FIG. 3 illustrates an example of a mission analytics dashboard ofperformance indicators for manned and unmanned vehicles over selectedtime periods.

FIG. 4 illustrates an example system to dynamically evaluate missionplans for manned and unmanned vehicles over selected current or futuretime periods.

FIG. 5 illustrates an example system to provide alternativevisualizations from a given route of manned and unmanned vehiclesutilizing 4D temporal data structures.

FIG. 6 illustrates a schematic diagram of an example system to providespatial and temporal forecasting for predictive situation awareness ofmanned and unmanned vehicles and to update contingent data withintemporal data structures.

FIG. 7 illustrates an example method to generate dynamic visualizationof performance indicators for manned and unmanned vehicles or manned andunmanned vehicle swarms over selected time periods.

FIG. 8 illustrates an example of the network topology and estimatedtiming parameters of a 4D data structure.

DETAILED DESCRIPTION

This disclosure relates to dynamic visualization of performanceindicators for manned and unmanned vehicles or manned and unmannedvehicle swarms over selected time periods. A dynamically generatedinterface visually provides mission status for current and/oralternative mission plans selected over the course of time. The statuscan be provided in real time for individual members of a swarm of mannedand unmanned vehicles and/or provided collectively for the swarm.Example status depicted in the interface can include current andpredicted fuel supply, payload action opportunities, payload actionstatus, time of arrival until next payload action, communicationsavailability, and weighted routing scores, among other status, forexample.

The swarm display setting specifies that the display objects in aformatted output file and their respective mission analytics are to beviewed across a swarm of the plurality of unmanned vehicles. Theindividual display setting specifies that the display objects in theformatted output file are to be viewed with respect to a selected memberof the swarm of the plurality of unmanned vehicles. A second interfaceselector specifies a subset of the display objects to be visualized inthe formatted output file. If the swarm display setting is selected, thesubset of display objects is generated having a specified analytic foreach member of the swarm. If the individual display setting is selected,the subset of display objects generated represents different analyticsfor a selected individual member of the swarm. Each of the specifieddisplay objects represent a current or future status of the missionanalytics over a specified time window.

The interface can include an analytics dashboard to enable manned andunmanned vehicle operators to rapidly conduct visual tradeoffs, betweencurrent and alternate mission plans as predicted over time. A timespecifier (e.g., time slider) can be provided with the interface toenable the operator to view current and predicted future system statusbased on interface movement of the slider. At a glance, the dashboarddisplays the projected fuel, payload action opportunities, payloadaction status, time of arrival (TOA) to next task, communicationsavailability, and an aggregate score for the current and alternatemission plans for example. The dashboard provides operators a completesystem view to help simplify complex mission scenarios and tradeoffsbetween different mission plan alternatives. Interface selection optionsare provided to mission plan operators to enable dynamicallyconstructing a desired view of selected mission analytics. In oneexample, operations can select a desired subset of mission analytics toview for a selected manned or unmanned vehicle. In another example, theoperator can dynamically configure a display where a common missionanalytic (e.g., task achievement) is viewed for a swarm of manned andunmanned vehicles. In yet another example of dynamic configuration, onemission analytic for a selected manned or unmanned vehicle may be viewedwhile concurrently viewing different analytics for one or more othermanned and unmanned vehicles in the swarm.

FIG. 1 illustrates an example system 100 for dynamic visualization ofperformance indicators for manned and unmanned vehicles or manned andunmanned vehicle swarms over selected time periods. As used herein, theterm unmanned vehicles can include unmanned ground vehicles (UGV),unmanned underwater vehicles (UUV), unmanned surface vehicles (USV), andunmanned aerial vehicles (UAV). Also, the term 4D refers to a datastructure or configuration where both three-dimensional positioncoordinates and with time accounted for as an additional dimension(e.g., 4^(th) dimension) for present and/or future visualizationsgenerated in the system. Also, the systems and components describedherein can be executed as computer readable components via one or moreprocessors that read the respective components from a non-transitorycomputer readable medium.

The computer-executable components include an analytics collector 110that receives world state data to provide status relating to a pluralityof mission analytics of an unmanned vehicle or an unmanned vehiclemission planner. The world state data can be received from world statefusion models such as generated at 620 of FIG. 6. The analyticscollector 110 can receive derived or computed metrics, real time states,and/or projected future states for a given mission. The missionanalytics relate to the ability of the unmanned vehicle (or vehicles)and its associated payload resources to execute a given mission plan. Anasset filter 114 filters the status from the analytics collector 110with respect to mission analytics of a subset of selected assets.

An asset selector 120 which is part of human machine interface (HMI) 124specifies the subset of selected assets to the asset filter 114. Ananalytic aggregator 130 collects the filtered status from the assetfilter 114 and generates a visual analytics file based on one or moreselected analytics for the subset of selected assets. The visualanalytics file can be stored in a visual analytic repository 134 whichcan store various analytics such as pie charts, bar graphs, alternativeroute displays, vehicle status, and so forth. The analytic aggregator130 can be a rule-based engine that operates for a selected individualunmanned vehicle asset or for a selected subset of assets.

An analytic selector 140 in the HMI specifies the one or more selectedanalytics to the analytic aggregator 130. A rendering pipeline 150processes the visual analytics file from the analytic aggregator 130 andgenerates a formatted output file describing a visualization of theplurality of mission analytics from the visual analytics file which canbe displayed via display 160. The formatted output file is generated asa collection of display objects. Each of the display objects representat least one mission analytic from the one or more selected analytics ofthe subset of selected assets. Each of the specified display objectsrepresents a current or future status of the mission analytics over aspecified time window which can be specified via time specifier 170 ofthe HMI 124.

The system 100 receive status from a plurality of status generators (Seee.g., FIG. 6) to provide status relating to a plurality of missionanalytics that enable mission plan operators to determine the ability ofan unmanned vehicle to execute a given mission. The status generatorscan include status from one or more unmanned vehicles and one or moreunmanned vehicle mission planners, for example. Each of the unmannedvehicles can be associated with at least one mission planner and oftentimes associated with a plurality of mission planners for differentportions of the unmanned vehicle route (e.g., one mission planner fortakeoff, one mission planner for vehicle tasking, and another missionplanner to return vehicle to base). The mission analytics relate to theability of the unmanned vehicles and their respective payload resourcesto execute a respective mission plan and/or respective alternativemission plan generated by the unmanned vehicle mission planners.

The formatted output file to the display 160 can be generated as acollection of display objects (e.g., XML graphical objects). Each of thedisplay objects represent at least one mission analytic from the missionanalytics and related to an individual or selected subset of a swarm.For example, one display object may represent a given fuel status for aspecified unmanned vehicle and another display object may represent thepayload collection capability of another unmanned vehicle. An interfaceselector specifies a swarm display setting or an individual displaysetting (See e.g., FIG. 2). The swarm display setting specifies that thedisplay objects in the formatted output file and their respectivemission analytics are to be viewed across a swarm of unmanned vehicles.The individual display setting specifies that the display objects in theformatted output file are to be viewed with respect to a selected memberof the swarm of the of the unmanned vehicles.

A graphical user interface (GUI) can be operated via the renderingpipeline 150 to display the mission analytics visualization output fromthe repository 134. The GUI provides a visualization of the displayobjects that represent a current or future status of the missionanalytics over a specified time window as specified by a time specified.In one example, the display objects represent at least one of a fuelstatus for an unmanned vehicle, a payload collection opportunity (e.g.,ability of a payload sensor to detect its intended sensing target) foran unmanned vehicle, or and a payload collection status (e.g., status ofsensor while collecting such as resolution and number of images beingcollected) for an unmanned vehicle in a present or future time window asspecified by the time slider. In another example, the display objectsrepresent mission analytics from at least one of time of arrival of nextpayload collections for an unmanned vehicle, a communicationsavailability for the unmanned vehicle (e.g., ability of unmanned vehicleto communicate with a given satellite), and a weighted route score foran unmanned vehicle relating to alternative routes for an unmannedvehicle. Alternative route generation and scoring are described in moredetail below with respect to FIGS. 4, 5, and 8.

In one specific example, the weighted route score can be stored in adata structure that includes a route field to describe a loaded routefor the unmanned vehicle. A behavior field in the data structuredescribes three-dimensional spatial capability of the unmanned vehicleand its resource capability along the loaded route that includes atleast one of a latitude, a longitude, and an altitude parameter for theloaded route of the unmanned vehicle. A when field in the data structuredescribes a starting time or a duration for the loaded route of theunmanned vehicle and a starting time or a duration for deployment of theresources that drive mission plan inputs for the unmanned vehiclemission planner. Mission analytics for the alternative routes of theunmanned vehicles can be projected over time via command inputs from thetime specifier 170. In yet another example, the display objects andassociated analytics can represent a status from at least one of aweather prediction model for an unmanned vehicle, an unmanned vehicletasking model, an unmanned vehicle threat detection model, and anunmanned vehicle tracking model, for example. Depending on theselections of the HMI 124, the analytic aggregator 130 receives operatorinput selections to dynamically specify which display objects togenerate in the formatted output file. The dynamically specified displayobjects are specified relative to a selected unmanned vehicle orspecified relative to a plurality of unmanned vehicles in a swarm, forexample.

The time specifier 170 specifies time projections of the loaded routeand/or alternative routes, specifies viewing of the mission analyticsover time, and specify deployment of resources over time. The timespecifier 170 can include a time slider graphical input to specify acurrent time or the future time for the visualization output on the GUI.In another example, the time specifier 170 utilizes a voice input tospecify the current time or the future time for the visualization outputon the GUI. Other examples include a hand or figure gesture to specifythe current time or the future time or a retinal scan input to specifythe current time or the future time for the visualization output on theGUI.

The analytics described herein can be projected over time for a selectedunmanned vehicle and/or from a swarm of unmanned vehicles. To facilitateprojections, a system as illustrated and described below with respect toFIG. 6 can be provided. The system includes a state aggregator toprocess input data feeds to update the behavior field of the 4D datastructure described herein in real time. The data feeds include at leastone of a tasking feed, a mission plan feed, a weather feed, a threatfeed, a sensor feed, a tracks feed, and a communications feed, forexample.

The system 100 includes a prediction engine to project the data feedsfrom the state aggregator into a future visualization of the loadedroute, a future visualization of the resources that drive the missionplan inputs of the respective mission planners, and a futurevisualization of at least one alternative route for the unmannedvehicle. This includes projecting analytics for a given loaded route oralternative routes for unmanned vehicles and/or unmanned vehicle swarmsover time. The system 100 can include a memory (not shown) to storecomputer-executable components (e.g., components 110 though 170) andinstructions. A processor (not shown) executes the computer-executablecomponents and instructions from the memory.

FIG. 2 illustrates an example mission analytics interface 200 fordynamic visualization of performance indicators and switching controlsfor changing views between unmanned vehicles or vehicle swarms overselected time periods. The mission analytics interface 200 can generatediffering type of visualizations shown at 210 and 220 depending onwhether individual unmanned vehicle analytics are selected or swarmanalytics are selected. As will be illustrated and described below, themission analytics output can be configured as an analytics dashboardwhere a collection of differing analytics (e.g., analytics related toone member or members of a swarm) can be visualized. The selectedanalytics can be represented as display objects in the visualizations210 and/or 220, where each of the display objects represent at least onemission analytic from a collection of mission analytics. For example,one display object may represent a given fuel status for a specifiedunmanned vehicle and another display object may represent the payloadcollection status of another unmanned vehicle. As shown, the interface200 can include a time specifier 230 to specify a current time statusfor a given analytic and/or specify a future projected status for agiven analytic and as visualized via the display objects.

An interface selector 240 specifies a subset of the display objects tobe visualized in the formatted output file generated by the statusformatter described above. This can include selecting icons representinga selected swarm member and selection icons representing desiredanalytics to be viewed. The subset of display objects generatedrepresents different analytics for a selected individual member of theswarm where its status can be viewed via visualization 210. Anotherselector 250 specifies that the visualization 220 is to be generatedhaving mission analytics and status for more than one member of a swarm.If the swarm display setting is selected at 250, the subset of displayobjects having an associated mission analytic are generated having aspecified analytic for each member of the swarm (or for each member ofselected subset of swarm). In this example, the selector at 250 caninclude icons representing multiple members of a swarm and iconsrepresenting mission analytic status to be viewed for the respectivemembers of the swarm. In some cases, status for the swarm can be for acommon analytic across the swarm (e.g., fuel consumption of each memberof the swarm) and/or status can be viewed as differing analytics foreach selected member of the swarm (e.g., fuel status for one member andtime of arrival for next collections status for another member).

As noted previously, the display objects in the visualization 210represent at least one of a fuel status for an unmanned vehicle, apayload collection opportunity (e.g., ability of a payload sensor todetect its intended sensing target) for an unmanned vehicle, or and apayload collection status (e.g., status of sensor while collecting suchas resolution and number of images being collected) for an unmannedvehicle in a present or future time window as specified by the timespecifier 230. In another example, the display objects represent missionanalytics from at least one of time of arrival of next payloadcollections for an unmanned vehicle, a communications availability forthe unmanned vehicle (e.g., ability of unmanned vehicle to communicatewith a given satellite), and a weighted route score for an unmannedvehicle relating to alternative routes for an unmanned vehicle.

In yet another example, the display objects and associated analytics canrepresent a status from at least one of a weather prediction model foran unmanned vehicle 114, an unmanned vehicle tasking model, an unmannedvehicle threat detection model, and an unmanned vehicle tracking model,for example. Depending on the selections of the selector 240 and/or 250,the mission analytics interface to can dynamically visualize whichdisplay objects to view for the operator. As shown at the visualization220, when swarm status is selected at 250, one or more analytics can bevisualized and shown as selected analytic for swarm member 1 thoughselected analytic for swarm member M, where M is a positive integer.

FIG. 3 illustrates an example of a mission analytics dashboard 300 ofperformance indicators for an unmanned vehicle over selected timeperiods. The dashboard 300 includes a time specifier 310 to specifycurrent and/or future status for selected analytics on the dashboard300. In this example, current and predicted fuel status for the currentroute of the unmanned vehicle and one or more alternative routes isshown. At 330, payload collection opportunities for a given payload areshown. In this example, alternate route A and Alternate Route B bothhave higher collection ability than the current route, thus the operatormay decide to switch the current route to one of the alternative routes.At 340, payload collection status is displayed. These can include thenumber of targets collected, the number of targets pending, and thenumber of targets missed, for example. At 350, the time of arrival untilnext payload collections can be displayed for the current route and/orone or more alternative routes. This can include projections from thestate aggregator and prediction engine described herein (See e.g., FIG.6). At 360, communications availability can be displayed and orprojected. This can include times when the unmanned vehicle is inproximity to a given communications asset along the current route and/oralternative routes. At 370, weighted route scores can be visualizedwhich show scoring for the current route and/or one or more alternativeroutes where the score reflects the effectiveness along a given route toutilize one or more of the unmanned vehicle assets and resources (Seee.g., FIG. 4 for scoring alternative routes and example FIG. 5 formanaging alternative routes).

The dashboard 300 can be dynamically configurable display historical,current, and/or future unmanned vehicle mission performance indicators.The dashboard 300 represents an example of a dynamically selected subsetof mission performance indicators that can be displayed to an operatorif so, selected for viewing (among other analytics not shown). With asingle glance, the operator can visualize analytical metrics, such ascommunications availability and payload collection opportunities over Nnumber of alternate mission scenarios. It is noted that theaforementioned analytics are merely a subset of indicators that can bedisplayed on the analytics dashboard 300. Substantially any data objectsor an aggregate of multiple data objects can be used for visualizationon the dashboard 300. Thus, an operator using the analytics dashboard300 can rapidly conduct visual tradeoffs, between the current andalternate routes, for example, along with respective analytics to gaugethe efficacy of a given route. At a glance, the dashboard 300 displaysthe projected fuel, payload collection opportunities, probabilities ofsurvival and detection, communications availability and an aggregatescore for the current and alternate route scenarios, for example. Thedashboard 300 provides the users a complete system view of selectedanalytics to simplify analysis of complex mission scenarios.

FIG. 4 illustrates an example system 400 to dynamically evaluate missionplans for unmanned vehicles over selected current or future timeperiods. The system 400 employs at least one external mission planner402 that processes one or more mission plan inputs 405 and generates amission definition file (MDF) 420 that includes mission data thatdescribes one or more tasks and routes for one or more vehicles. Asshown, the mission plan inputs 405 can also include task priorities andconstraints. The system 400 includes a task achievement evaluator 430that employs a plurality of evaluation engines, shown as task typeevaluator 1 though N, where N is a positive integer, to evaluate eachplanned task contained in the MDF with respect to its respective tasktype scoring criteria 434 to generate individual task quality scores 436representing an expectation of how well the planned task will beexecuted. Task type scoring criteria 434 are defined generically foreach type of task planned and may be further constrained by optionalinputs accompanying the tasking 405. For example, the general task typescoring criteria 434 for an image collection task may include items suchas image target location and a required image quality whereas thegeneral task type scoring criteria for an area search task may include atime of arrival, search duration, and geographical area within which tosearch.

For a specific image collection task, for example, the specific criteria434 can specify the latitude, longitude, and elevation of the desiredimaging target and a NIIRS 7 (National Imagery Interpretability RatingScale level 7) as the image quality requirement. In addition, a routecost evaluator 437 evaluates the cost of each route 438 contained in theMDF 420. Route costs can be defined as time spent, fuel consumed,amounts of other consumables used, or threats to vehicle safety, forexample.

The system 400 also includes a 4D mission plan constructor 440 thatconstructs a 4D data structure 445 making time an explicit parameter incombination with; the input mission plan 420; individual route costs438; individual task quality scores 436; and the original input tasking,task priorities, and constraints 405. The 4D mission plan constructor440 also reviews and annotates the 4D mission plan identifying portionsof the plan that may need operator attention. This process is performedfor at least the primary mission plan and all optional plan alternativescontained within the MDF 420. An annotated 4D mission plan 445 is outputby the constructor 440 use by a plan comparator 450.

The data structures 445 can be expressed as a multi-dimensional tuple(e.g., What, <Behavior, When>) and configured as a graph. The “What”argument of the data structure 445 provides the labeled route anduniquely identifies it (e.g., primary route, contingency route, optionalbranch, and so forth). The “Behavior” argument provides the 3D spatialcapability (e.g., <latitude, longitude, altitude>, heading, speed, fuelremaining) and the “When” argument provides the temporal capability(e.g., start time, time of arrival, and/or time tolerance (+/−minutewindow)). The data structure 445 and generated visualizations describedherein empowers operators to conduct “What-If” scenarios of multipleassets across multiple routes by significantly reducing the burden ofcomplexity for the operator. Effectively, this capability enablesoperators to rapidly conduct visual tradeoffs for unmanned systems,manned systems, and manned-unmanned teams, for example. Each Behaviorfield is associated with a When field, expressed as a starting timeand/or duration. Also, any path in the graph data structure for thevisualization can be represented as a sorted linked list, for example,representing a primary unmanned vehicle route and any alternativeroutes. In one example, the 4D mission plan constructor 440 employs asorted linked list to represent the primary route and the alternativeroute and to generate a fused visualization of the primary route and thealternative route on a human machine interface (HMI) 452 of the plancomparator 450.

The plan comparator 450 enables an operator 460 to directly compare 462alternative mission plans 445 against each other, via the HMI 452,identifying poor performing segments of each plan that may needadjustment or selecting a specific plan option that is more desirablethan the other options. For example, the quality of planned imagecollections may be better in one plan option than another. For multiplevehicles, the plan comparator 450 provides functions that identify areasof conflict between plans as well as opportunities for collaborationbetween vehicles. The HMI includes a Graphical User Interface (GUI) thatprovides interactive visualizations to the operator. Thesevisualizations can be generated for current or future time periodsspecified by the operator via a time selector embedded in the GUI. Aspatial-temporal forecaster 454 in the plan comparator 450 generatespredictions of the current and future states of the mission for the GUIto visualize based on the current time or the future time specified bythe time selector. The plan comparator 450 also stores the plans andplan segments into an archive 456 for later retrieval and reuse. Ifadditional plan alternatives are desired, the operator 460 makesadjustments to the input parameters of the mission planner 402requesting generation of additional plan options 420. The internalfunctions of the plan comparator 450 can be manual, semi-automated, orfully automated as needed. The output of the plan comparator 450 is aselected mission plan for a single vehicle, a set of selected plans fora single vehicle, or a set of selected plans for multiple vehicles shownat 458.

FIG. 5 illustrates a system 500 to provide alternative visualizationsfrom a given route of an unmanned vehicle utilizing 4D temporal datastructures. The system 500 includes two or more cooperating modules togenerate the temporal data structures described herein. These modulesinclude a 4D mission plan constructor 510 and at least one independentmission planning system 512 that generates one or more mission plans forone or more vehicles. The output of the mission planning system 512 is aMission Data File (MDF) 514 that includes at least one primary missionplan for at least one vehicle. The MDF 514 may include multiple missionplan alternatives, including contingency plans that account for variousvehicle failure scenarios, in addition to the primary mission plan. Inanother example, there may exist multiple independent mission planningsystems 512 each producing one or more mission plans and alternativesfor one or more vehicles of various types and in various domains (e.g.,land, sea, air, space).

Each mission planning system 512 generates its plans driven by inputtasking 516 provided by an external entity. For military operations,tasking inputs 516 may come from a central command authority while forcommercial operations, tasking may come from a central or distributedorder processing system. In most examples, specific tasking 516 isspecified to the mission planning system 512 along with associatedtasking priorities and optional additional tasking constraints.Execution timing is often an additional constraint placed on inputtasking requests 516 and can take the form of specifying aNo-Sooner-Than (NST) time of arrival, a No-Later-Than (NLT) delivery ofa package, or a fixed time window within which a task may only beperformed including both NST and NLT constrained situations.

In addition to tasking requests, priorities, and constraints 516,environmental planning data 518 is also used by the mission planningsystem 512 when generating a mission plan. Environmental planning data518 can include orbital models and gravitational field models for spacevehicles, wind velocity vectors, air temperature, and pressure for airvehicles, sea state and sea currents for sea surface vehicles, seatemperature and currents for undersea vehicles, and/or terrain modelsand road networks for ground vehicles, for example.

The 4D mission plan constructor 510 of system 500 uses the inputtasking, priorities and constraints 516, the environmental planning data518, along with the mission plans contained in the mission data files514 output by the one or more mission planning systems 512 to construct4D mission plan data structures 519. The mission data contained withinthe MDF 514 is parsed by one or more MDF parsers 520. Since eachindependent mission planning system 512 will likely output a differentMDF format, the MDF parser 520 can parse each of the formats (e.g., viamapping tables that map one MDF data entry to a common data format). Asthe mission data is parsed from the MDF 514, an initial 4D datastructure is populated with the mission data that exists.

Since conventional mission planners 512 do not output time as anexplicit parameter of their mission plans, time extrapolation engines530 can be employed to estimate the intermediate times of arrival forvehicles from a current location or waypoint to a subsequent location orwaypoint. These engines 530 can include but are not limited to, Kalmanfilters, Markov filters, Particle filters, Runga-Kutta,Predictor-Corrector, and so forth, for example. The time extrapolationengines 530 can employ vehicle models 540 and environmental models 550as needed. Vehicle models 540 are vehicle specific and model thephysical ability of the vehicle to move over time. Environmental models550 model environmental effects on vehicle movement. For example, a windforecast model consisting of wind direction and velocity at mission timein the mission area can be used together with a vehicle model 540 by thetime extrapolation engine 530 to estimate times of arrival of a specificair vehicle traveling on its planned route. A system for updating suchenvironmental vehicle models is illustrated and described below withrespect to FIG. 6.

After completing the estimation of the timing parameters in the 4Dmission plan data structure 519, the 4D mission plan constructor 510adds in the timing constraints specified in the input tasking 516 to thedata structure. While not every task in the mission plan will have atiming constraint, those that do can be incorporated into the 4D datastructure 519. After completing adding the timing constraints to the 4Ddata structure, the 4D mission plan data structure 519 is output fromthe 4D mission plan constructor 510 where it can be employed to generatevisualizations of alternative plans for analysis as described herein.

FIG. 6 illustrates a schematic diagram of an example system 600 toprovide spatial and temporal forecasting for predictive situationawareness of unmanned vehicles and to update contingent data withintemporal data structures described herein. The system 600 provides amore detailed aspect to the system 100 described above. The system 600includes a human machine interface (HMI) 610, a state aggregator 614,and a prediction engine 618. The HMI 610 includes a world staterendering pipeline 620 to generate visualizations to an output device624 that can be viewed by an operator 626. A graphical input 628 can beused by the operator to operate a time slider to specify current orfuture times for visualization renderings at the display 624.

The state aggregator 614 receives and processes the various data feedsthat constitute a mission. It is generally always on and is processingin real time. It provides the current world state to the HMI 610 and theprediction engine 618. The HMI 610 provides situational awareness inreal time of the current world state and can provide fusedvisualizations of the current world state and alternative future worldstates. The HMI 610 also includes graphical tools 628 to allow anoperator to control the prediction engine 618 enabling the generation ofalternative plans projected into the future. The prediction engine 618has access to the current world state, alternative plans, and receivesoperator inputs to project the current world state and alternative plansto some point in the future. The prediction engine 618 provides theprojected future world state and any projected alternatives to the HMI610 for fusion and display to the operator 626.

The state aggregator 614 includes static and real-time data feeds thatconstitute a mission and are received via adapter modules 630 which areprocessed via world state manager 634. The data feeds can include amission plan. This data feed originates from various mission planningengines in various formats, and includes the primary route andcontingency routes for each platform. Furthermore, it includes the stateof a platform (position, velocity, orientation, fuel, and so forth) atperiodic increments. This is true for each unmanned vehicle platformalong the primary route and contingency routes.

A tasking data feed originates external to the system in various formatsand consists of actions to perform. These actions may includeIntelligence Surveillance and Reconnaissance (ISR) collections, strike(both kinetic and electronic), communications relay, and the delivery ofa payload such as fuel or supplies. Tasking can have constraints thatmay include a time window within which the action must take place; alocation where the action must be performed; and detailed geometricconstraints that may include azimuth, elevation, and velocity withrespect to the tasking, for example. A weather data feed originatesexternal to the system in various formats and consists of informationpertaining to winds, currents, cloud coverage, temperature, humidity,and precipitation. Weather feeds include both current measurements andforecasted conditions.

A threats data feed originates external to the system in various formatsand includes threat information (e.g., type of threat, location, andrange of threat effectiveness). Threats may also include environmentalobstacles such as terrain, bodies of water, icebergs, and culturalobstacles such as power lines, buildings, and towers. A tracks data feedoriginates external to the system in various formats and includes trackinformation (e.g., entity type, hostile/friendly, location, velocity,orientation). A sensors data feed is per payload type for each asset andconsists of data for field of view and field of regard data on positionand orientation of the sensor. A COMMS data feed provides they type ofcommunications asset available and its current and planned footprintover time.

Because each of the data feeds delineated above has multiple formats andcan change in real-time, and independently of one another, adapters 630have been created to mediate the various data types to an internalstandardized data model managed by the world state manager 634. Theworld state manager 634 has the responsibility to aggregate thedisparate data feeds into a single internal representation and at asingle point in time as well as store and manage the internal datamodel. By extension, other inputs and data feeds may also be aggregatedinto the internal data model as needed through additional adapters 630.

At a particular time, instance, t(i), the aggregation of all the data ascaptured constitutes the present world state 640 which can be utilizedto update contingency data within the data structures described herein(e.g., via the behavior fields). This present world state 640 is updatedin real time and, at a minimum, is updated at a 1 Hz rate, for example.The present world state snapshot is made available to the HMI's fusionand rendering pipeline component 620 which automatically renders thestate onto a physical display device for the operator 626 to view. Thepresent world state 634 is also provided to the prediction engine 618for propagation into some future time t(f) specified by the operator.

As noted above, the HMI 610 includes the world state fusion & renderingpipeline 620 which is an event driven component that receives worldstate updates or changes made by the state aggregator 614 to the presentworld state 614. It also has the responsibility of fusing the currentworld state with future world state generated by the prediction engine618 and rendering the resultant fused information onto the physicaldisplay device 624 for the operator 626 to view. The graphical inputtools 628 houses graphical components that take inputs from an operator626. A notional example of an input tool is a timeline slider. Thisinput component displays current time, which is always moving forward,and a future time, which is calculated by an offset that is determinedby the operator through sliding a marker along the timeline, in oneexample. When the operator 626 requests a projection of the world stateby method of a graphical input tool 628, the present world state 640 andthe offset time are passed into the prediction engine 618 which respondswith a projection of the present world state 640 and its alternativesinto the specified future point in time S(t(f)) where S is the projectstate at future time t(f).

The prediction engine 618 includes a time manager 650 that is the mastertime manager for all visual components of interest. It maintains amaster wall clock and a real-time clock for each platform and entity ofinterest. When it receives an offset time instance, it calculates afuture clock instance by adding the real-time (current time) to theoffset time for each platform or entity of interest. Since real timeconsistently moves forward, the time manager 650 has the addedresponsibility of determining how long the projected state is valid for.After the valid time period has expired, the time manager 650 notifiesthe system that the projected future world state is no longer valid andthat a new projection is required.

One or more object model estimators (or predictors) 654 can be providedas a plug-in framework for creating, loading, and managing behavioralmodels. These models have a one to one correspondence to the data feedscoming into the state aggregator 614. Each object model estimator 654includes methods and constraints specific to its associated data feedfor projecting that particular data feed into the future. Such methodsmay include a family of prediction and interpolation algorithms that areappropriate for each respective object behavioral model. The predictionalgorithms can include, but are not limited to, Kalman filters, Markovfilters, Particle filters, Runga-Kutta, Predictor-Corrector, and soforth.

A world state propagator 656 processes an offset time, current state,and the appropriate object model estimators, and propagates them fromthe present state to a specified future state. This component 656conducts this action for each object model. Upon completion of all themodels, the predicted world state is aggregated at 660 and sent to theworld state fusion & rendering pipeline 620 for display at 624. Outputfrom the predicted world state at 660 can also be utilized to updatecontingency data within the data structures described herein (e.g., viathe behavior fields).

In view of the foregoing structural and functional features describedabove, an example method will be better appreciated with reference toFIG. 7. While, for purposes of simplicity of explanation, the method isshown and described as executing serially, it is to be understood andappreciated that the method is not limited by the illustrated order, asparts of the method could occur in different orders and/or concurrentlyfrom that shown and described herein. Such method can be executed byvarious components configured in an integrated circuit, processor, or acontroller, for example.

FIG. 7 illustrates an example method 700 to generate dynamicvisualization of performance indicators for unmanned vehicles or vehicleswarms over selected time periods. At 710, the method 700 includesproviding status relating to a plurality of mission analytics of anunmanned vehicle or an unmanned vehicle mission planner (e.g., viastatus generators 110 of FIG. 1). The mission analytics relate to theability of the unmanned vehicle and its associated payload resources toexecute a given mission plan. At 720, the method 700 includes generatingan analytics file from the status describing the plurality of missionanalytics (e.g., via status aggregator 120 of FIG. 1). At 730, themethod 700 includes generating a formatted output file from theanalytics file describing a visualization of the plurality of missionanalytics from the analytics file (e.g., via status formatter 130 ofFIG. 1). The formatted output file can be generated as a collection ofdisplay objects.

Each of the display objects represents at least one mission analyticfrom the plurality of mission analytics. At 740, the method 700 includesspecifying a subset of the display objects to be visualized in theformatted output file (e.g., via selector 140 and/or 150 of FIG. 1). At750, the method 700 includes generating a mission analyticsvisualization output from the subset of display objects specified viathe formatted output file (e.g., mission analytics output generator 160of FIG. 1). Each of the specified display objects represents a currentor future status of the mission analytics over a specified time window(e.g., via time specifier 190 of FIG. 1). Although not shown, the method700 can also include projecting mission analytics for alternative routesfor an unmanned vehicle or an unmanned vehicle swarm over the specifiedtime window.

FIG. 8 illustrates an example of the network topology 800 and estimatedtiming parameters of a 4D data structure 808. It is noted that thenetwork topology 800 illustrated is merely one specific example forillustrative purposes. Numerous other network topologies are possible inaccordance with the present disclosure. The network topology 800 can bevisualized via graphical user interface (not shown) that plotsalternative mission plans via the temporal data structures describedherein. The network topology of 800 shows a set of waypoints labeled wp1through wp8. Waypoint wp1 is the start of the network and waypoints wp5and wp8 are terminating waypoints. The waypoints are specified in awaypoint list 810 as a tuple wherein each waypoint has a specified 3Dlocation (latitude, longitude, and altitude). The waypoints of thenetwork 800 are connected by route segments labeled s1 through s8 andare specified on a route segment list 820. Each route segment of 820 isdefined as a directed path between two waypoints and an EstimatedTransit Time (ETT). The route segment direction is determined in theorder of the waypoints defined. For example, route segment s1 of list820 is a directed segment from wp1 to wp2 with an estimated transit time(ETT) of ETT1.

The network topology of 800 defines three routes in this example: R1,R2, and R3, however more or less routes are possible. Each route isspecified in a route list 830 by an ordered list of route segments. Theroutes specified in the route list 830 consist of at least one primaryroute and optional alternative routes that can be contingency routes orsimple alternatives to the primary plan should certain conditions becomesatisfied during mission execution. In the example of FIG. 8, route R1could represent the primary route consisting of the ordered segments s1,s2, s3, s4. These ordered segments specify the ordered waypointdestinations wp1, wp2, wp3, wp4, and terminating at wp5. In thisexample, route R2 of the route list 830 could represent a mission planalternative that would be chosen for execution should certain conditionsbecome true during mission execution. Route R2 specifies ordered routesegments s1, s2, s5, s6, and s7 ending at the same terminating waypointwp5 as the primary route R1. Finally, route R3 of 830 could represent acontingency route that would be chosen for execution should certainconditions become true during mission execution. Route R3 specifiesordered route segments s1, s2, s5, s6, and s8 ending at a differentterminating waypoint wp8 that could represent the contingency recoverylocation for the vehicle, for example. Estimated times of arrival (ETA)for each waypoint is calculated by the route traversed to arrive at thatwaypoint. For example, the ETA for waypoint wp5 is different for routeR1 than for route R2 which traverses different segments to arrive at therespective location.

Another construct of the network topology 800 is a route leg list 840.The route leg list 840 enables the specification of portions of thenetwork topology 800 that represent specific activities of the missionplan that the operator wants to pay additional attention to and perhapsperform additional analysis of. In the example of FIG. 8, route leg L1specifies segments s1 and s2 which are common to every other option inthe plan. Route leg L2 specifies segments s1, s2, s5, and s6 whichdefine the optional plan leg up to the decision waypoint wp7 where thedecision must be made to either complete the optional mission plan andterminate at waypoint wp5, or execute the contingency plan and terminateat waypoint wp8, for example.

What has been described above are examples. It is, of course, notpossible to describe every conceivable combination of components ormethodologies, but one of ordinary skill in the art will recognize thatmany further combinations and permutations are possible. Accordingly,the disclosure is intended to embrace all such alterations,modifications, and variations that fall within the scope of thisapplication, including the appended claims. As used herein, the term“includes” means includes but not limited to, the term “including” meansincluding but not limited to. The term “based on” means based at leastin part on. Additionally, where the disclosure or claims recite “a,”“an,” “a first,” or “another” element, or the equivalent thereof, itshould be interpreted to include one or more than one such element,neither requiring nor excluding two or more such elements.

What is claimed is:
 1. A system, comprising: a memory to storecomputer-executable components; and a processor to execute thecomputer-executable components from the memory, the computer-executablecomponents comprising: an analytics collector that receives world statedata to provide status relating to a plurality of mission analytics of aplurality of unmanned vehicles; an asset filter to filter the statusfrom the analytics collector with respect to mission analytics of asubset of selected assets corresponding to a subset of unmanned vehiclesof the plurality of unmanned vehicles; an analytic aggregator to collectthe filtered status from the asset filter and to generate a visualanalytics file based on selected mission analytics of the plurality ofmission analytics for the subset of unmanned vehicles; and a renderingpipeline to process the visual analytics file from the analyticaggregator and to generate a formatted output file describing avisualization of the plurality of mission analytics from the visualanalytics file, the formatted output file generated as a collection ofdisplay objects, wherein the display objects include a first displayobject that represents at least one mission analytic of the selectedmission analytics for a first unmanned vehicle of the subset of unmannedvehicles and a second display object that represents at least onemission analytic of the selected mission analytics for a second unmannedvehicle of the subset of unmanned vehicles different from the at leastone mission analytic for the first unmanned vehicle.
 2. The system ofclaim 1, further comprising a graphical user interface (GUI) to displaythe first and second display objects of the display objects from theformatted output file concurrently on a display, wherein the first andsecond display objects represent a current or future status of the atleast one mission analytic over a specified time window.
 3. The systemof claim 2, wherein the at least one mission analytic for each of thefirst and second unmanned vehicles represent at least one of a fuelstatus for an unmanned vehicle, a payload collection opportunity for theunmanned vehicle, and a payload collection status for the unmannedvehicle in a future time window.
 4. The system of claim 2, wherein theat least one mission analytic for each of the first and second unmannedvehicles represent at least one of time of arrival of next payloadcollections for an unmanned vehicle, a communications availability forthe unmanned vehicle to communicate with a given satellite along acurrent route relative to alternative routes for an execution of amission, and a weighted route score for the unmanned vehicle relating tothe current route and the alternative routes for the unmanned vehicle.5. The system of claim 4, wherein the weighted route score is stored ina data structure comprising: a route field to describe a loaded routefor the unmanned vehicle; a behavior field to describe three-dimensionalspatial capability of the unmanned vehicle and its resource capabilityalong the loaded route that includes at least one of a latitude, alongitude, and an altitude parameter for the loaded route of theunmanned vehicle; and a when field that describes a starting time or aduration for the loaded route of the unmanned vehicle and a startingtime or a duration for deployment of the resources that drive missionplan inputs for an unmanned vehicle mission planner.
 6. The system ofclaim 4, wherein mission analytics for the alternative routes areprojected over time.
 7. The system of claim 2, wherein the displayobjects represent a status from at least one of a weather predictionmodel for an unmanned vehicle, an unmanned vehicle tasking model, anunmanned vehicle threat detection model, and an unmanned vehicletracking model.
 8. The system of claim 2, wherein the display objectsare specified relative to the subset of unmanned vehicles in a swarm. 9.The system of claim 5, further comprising a time specifier to specifytime projections of the loaded route, specify viewing of the pluralityof mission analytics over time, and specify deployment of resources overtime, the time specifier comprises a time slider graphical input tospecify a current time or the future time for the visualization outputon the GUI, a voice input to specify the current time or the future timefor the visualization output on the GUI, a hand or figure gesture tospecify the current time or the future time for the visualization outputon the GUI, or a retinal scan input to specify the current time or thefuture time for the visualization output on the GUI.
 10. The system ofclaim 5, further comprising a state aggregator to process input datafeeds to update the behavior field of the data structure in real time,the data feeds include at least one of a tasking feed, a mission planfeed, a weather feed, a threat feed, a sensor feed, a tracks feed, and acommunications feed.
 11. The system of claim 10, further comprising aprediction engine to project the data feeds from the state aggregatorinto a future visualization of the loaded route, a future visualizationof the resources that drive the mission plan inputs, and a futurevisualization of at least one alternative route for the unmannedvehicle.
 12. A non-transitory computer readable medium having computerexecutable instructions stored thereon, the computer executableinstructions to: provide status relating to a plurality of missionanalytics of a plurality of unmanned vehicles and a plurality ofunmanned vehicle mission planners, the plurality of mission analyticsrelate to the ability of the plurality of unmanned vehicles and theirrespective payload resources to execute a respective mission plan or arespective alternative mission plan generated by the plurality ofunmanned vehicle mission planners; filter the status with respect tomission analytics of a subset of selected assets corresponding to asubset of unmanned vehicles of the plurality of unmanned vehicles;collect the filtered status from the asset filter and generate a visualanalytics file based on selected mission analytics of the plurality ofmission analytics for the subset of unmanned vehicles; and process thevisual analytics file and to generate a formatted output file describinga visualization of the plurality of mission analytics from the visualanalytics file, the formatted output file generated as a collection ofdisplay objects, the display objects including a first display objectthat represents at least one mission analytic of the selected missionanalytics for a first unmanned vehicle of the subset of unmannedvehicles and a second display object that represents at least onemission analytic of the selected mission analytics for a second unmannedvehicle of the subset of unmanned vehicles different from the at leastone mission analytic for the first unmanned vehicle.
 13. The computerreadable medium of claim 12, further comprising instructions to displaythe first and second display objects of the display objects from thevisual analytics file, the instructions provide a visualization of thefirst and second display objects of the display objects that represent afuture status of the selected mission analytics over a specified timewindow.
 14. The computer readable medium of claim 13, wherein the atleast one mission analytic for each of the first and second unmannedvehicles represent at least one of a fuel status for an unmannedvehicle, a payload collection opportunity for the unmanned vehicle, anda payload collection status for the unmanned vehicle in a present orfuture time window.
 15. The computer readable medium of claim 13,wherein the at least one mission analytic for each of the first andsecond unmanned vehicles represent at least one of time of arrival ofnext payload collections for an unmanned vehicle, a communicationsavailability for the unmanned vehicle to communicate with a givensatellite along a current route relative to alternative routes for anexecution of a mission, and a weighted route score for the unmannedvehicle relating to the current route and the alternative routes for theunmanned vehicle.
 16. The computer readable medium of claim 15, whereinthe weighted route score is stored in a data structure comprising: aroute field to describe a loaded route for the unmanned vehicle; abehavior field to describe three-dimensional spatial capability of theunmanned vehicle and its resource capability along the loaded route thatincludes at least one of a latitude, a longitude, and an altitudeparameter for the loaded route of the unmanned vehicle; and a when fieldthat describes a starting time or a duration for the loaded route of theunmanned vehicle and a starting time or a duration for deployment of theresources that drive mission plan inputs for a respective unmannedvehicle mission planner.
 17. The computer readable medium of claim 15,wherein mission analytics for the alternative routes are projected overtime.
 18. The computer readable medium of claim 13, wherein the displayobjects represent a status from at least one of a weather predictionmodel for an unmanned vehicle, an unmanned vehicle tasking model, anunmanned vehicle threat detection model, and an unmanned vehicletracking model.
 19. A computer implemented method, comprising: providingstatus relating to a plurality of mission analytics of a plurality ofunmanned vehicle or a plurality of unmanned vehicle mission planners,the plurality of mission analytics relate to the ability of a respectiveunmanned vehicle of the plurality of unmanned vehicles and itsassociated payload resources to execute a given mission plan; generatingan analytics file from the status describing the plurality of missionanalytics; generating a formatted output file from the analytics filedescribing a visualization of the plurality of mission analytics fromthe analytics file, the formatted output file generated as a collectionof display objects, each of the display objects represent at least onemission analytic from the plurality of mission analytics; and generatinga mission analytics visualization output from a subset of displayobjects of the display objects specified via the formatted output file,the subset of display objects including a first display object thatrepresents at least one mission analytic of the plurality of missionanalytics for a first unmanned vehicle of the plurality of unmannedvehicles and a second display object that represents at least onemission analytic of the plurality of mission analytics for a secondunmanned vehicle of the plurality of unmanned vehicles different fromthe at least one mission analytic for the first unmanned vehicle. 20.The method of claim 19, projecting mission analytics for alternativeroutes for an unmanned vehicle or an unmanned vehicle swarm over aspecified time window.